In this paper, we document wide regional disparities in economic activity and infrastructure. These disparities, especially between the north and the south, were partly determined by the regional development policy. The paper also examines empirically the contribution of agglomeration economies to labor productivity––and therefore to urban dynamics––using a recent panel data from Côte d’Ivoire for the period from 1980 to 1996.
The analysis indicates significant urbanization economies, notably those related to infrastructure, but the size of these economies varies across sectors and activities. In addition to providing linkages between markets, roads are critical in fostering dynamic growth of the urban areas in the hinterland, resulting in the concentration of economic activities.
Localization economies also stimulate industrial productivity. And yet, as the poor growth record of Côte d’Ivoire in this period shows, the country failed to take advantage of these economies.
Its declining capital stock, including infrastructure, may have contributed to the overall economic decline.
The paper shows, for example, that inadequate road infrastructure was an important constraint to economic activity. This especially so in the poorer regions of the north. Inadequate roads clearly constrained the productivity of primary (agriculture and resource extraction) and tertiary (services) industries that take up the bulk of the total economic activity.
Effects of congestion of roads on productivity in primary and tertiary sectors suggest that greater investment in road infrastructure is needed. This especially so in the poor regions oriented towards agriculture and the tertiary sector, which happen to be located in the north.12 Such infrastructure investments could have positive effects on productivity and urban and regional growth: (i) effects stemming from improving the collection and transport of agricultural products from the hinterland to centers of regional and sub-regional markets; (ii) effects arising from reduced delays and costs of access to markets, higher producers’ farm-gate prices because of lower transaction costs, and (iii) demand effects stemming from the intensification of trade flows with neighboring countries in the north. Also, in the rural environment, higher producer prices and a policy of ensuring access to health and education infrastructure constitute an important instrument for promoting faster human capital accumulation with direct effects on productivity, incomes, and poverty reduction.
12 Henderson (2000), for example, estimates that increased road density (measured by an increase of one standard deviation of road density) has the potential to raise the annual average growth rate in low income countries by about
¼ of 1 percentage point.
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Annex 1: Two digit economic activity classification in Côte d’Ivoire
Sector Number Economic activity definition
01 Feeding agriculture, livestock and hunting products Primary sector 02 Agricultural products for industry and exports
03 Timber products 04 Fishery products 05 Mining products
06 Seeds (grain and flour products) 07 Canned food processed
08 Beverages and ice foods 09 Fat foods
10 Other foods, tobbacco 11 Textile products 12 Leather products and shoes Secondary sector 13 Wood processed products
14 Produits pétroliers 15 Chemical products 16 Rubber processed products 17 Engineering works and glassware 18 Raw Metals
19 Transport materials
20 Other mechanical and electric products 21 Other industrial products
22 Electricity, gas and water 23 Construction
24 Transport and telecommunication 25 House renking and managing 26 Other services
Tertiary sector 27 Trade
28 Banking services
29 Banking service related products 30 Insurance services
31 Public administration services 32 Private administration services 33 Housekeeping services
Annex 2
ECONOMIC ACTIVITY LOCATION COEFFICIENTS (1980-1996)
Sector 01 g= {agn, lac, mco, sav} Sector 11 g= {agn, val} Sector 21 g= {lag}
g Other regions Total g Other regions Total g Other regions Total
01 990873392.9 2422236713 3413110106 11 71641553321 29559579446 1.01201E+11 21 20972202657 63927984.64 21036130642
Other sectors 19096738138 1.40126E+12 1.42036E+12 Other sectors 56962011792 1.26561E+12 1.32257E+12 Other sectors 1.22832E+12 1.74413E+11 1.40274E+12
Total 20087611531 1.40368E+12 1.42377E+12 Total 1.28604E+11 1.29517E+12 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12
E01 942718756.7 E11 62500465347 E21 2513956848
E01* 3404928094 E11* 94007798299 E21* 20725323331
S01 0.276868918 S11 0.664843412 S21 0.1212988
Sector 02 g= {agn, sav, sco, hsa} Sector 12 g= {lag} Sector 22 g= {lag}
g Other regions Total g Other regions Total g Other regions Total
02 3536213498 11507155455 15043368954 12 4230145459 0 4230145459 22 1.55846E+11 0 1.55846E+11
Other sectors 21741303980 1.38699E+12 1.40873E+12 Other sectors 1.24507E+12 1.74477E+11 1.41954E+12 Other sectors 1.09345E+12 1.74477E+11 1.26793E+12
Total 25277517478 1.39849E+12 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12
E02 3269134934 E12 518385629.1 E22 19098186381
E02* 14884422910 E12* 4217577345 E22* 1.38787E+11
S02 0.219634644 S12 0.122910758 S22 0.137608114
Sector 03 g= {agn, bas, lac, mon, nzi, sba, hsa} Sector 13 g= {agn, bas, mco, nzi, sba, hsa} Sector 23 g= {lac, lag, wor, hsa}
g Other regions Total g Other regions Total g Other regions Total
03 2183168670 4782753571 6965922241 13 7343107816 31328706778 38671814594 23 77561075715 1594397522 79155473237
Other sectors 50086576992 1.36672E+12 1.41681E+12 Other sectors 41579237852 1.34352E+12 1.3851E+12 Other sectors 1.18182E+12 1.62796E+11 1.34462E+12
Total 52269745662 1.3715E+12 1.42377E+12 Total 48922345668 1.37485E+12 1.42377E+12 Total 1.25938E+12 1.64391E+11 1.42377E+12
E03 1927434644 E13 6014302583 E23 7545007723
E03* 6931840894 E13* 37621429358 E23* 74754776855
S03 0.278055234 S13 0.159863745 S23 0.100930108
Sector 04 g= {lag} Sector 14 g= {lag} Sector 24 g= {lag, mar, zan}
g Other regions Total g Other regions Total g Other regions Total
04 6061015835 0 6061015835 14 8842457222 0 8842457222 24 1.94396E+11 3212596922 1.97609E+11
Other sectors 1.24323E+12 1.74477E+11 1.41771E+12 Other sectors 1.24045E+12 1.74477E+11 1.41493E+12 Other sectors 1.05571E+12 1.70458E+11 1.22616E+12
Total 1.2493E+12 1.74477E+11 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12 Total 1.2501E+12 1.7367E+11 1.42377E+12
E04 742750701.4 E14 1083604050 E24 20891547088
E04* 6035214014 E14* 8787540395 E24* 1.70182E+11
S04 0.123069488 S14 0.123311416 S24 0.122759789
Sector 05 g= {lag, mon} Sector 15 g= {lag} Sector 25 g= {lag}
g Other regions Total g Other regions Total g Other regions Total
05 55799860856 26201013.23 55826061869 15 44470724653 744445472.3 45215170126 25 2181445620 3687843.66 2185133464
Other sectors 1.19734E+12 1.70609E+11 1.36795E+12 Other sectors 1.20482E+12 1.73733E+11 1.37856E+12 Other sectors 1.24711E+12 1.74473E+11 1.42159E+12
Total 1.25314E+12 1.70635E+11 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12
E05 6664402301 E15 4796473780 E25 264090604.2
E05* 53637123712 E15* 43779258058 E25* 2181779832
S05 0.124249808 S15 0.109560417 S25 0.121043655
Sector 06 g= {bas,lac,den,mar,mco,nzi,sav,sba,zan,hsa} Sector 16 g= {bas, mco, hsa} Sector 26 g= {lac, lag, sco}
g Other regions Total g Other regions Total g Other regions Total
06 3943315069 21802900920 25746215989 16 9432178245 15567601015 24999779260 26 98265545114 1689347455 99954892569
Other sectors 36984090019 1.36104E+12 1.39803E+12 Other sectors 24462244224 1.37431E+12 1.39877E+12 Other sectors 1.15367E+12 1.70152E+11 1.32382E+12
Total 40927405088 1.38284E+12 1.42377E+12 Total 33894422469 1.38988E+12 1.42377E+12 Total 1.25193E+12 1.71842E+11 1.42377E+12
E06 3203220657 E16 8837031697 E26 10374664761
E06* 25280644557 E16* 24560812278 E26* 92937631727
S06 0.126706447 S16 0.359802094 S26 0.111630397
Sector 07 g= {lag, mco} Sector 17 g= {bas, lag} Sector 27 g= {bas, lac, lag, mon, den, mar, mco, n’zi, sav,
g Other regions Total g Other regions Total sba, wor, zan, sco, hsa}
07 50298450578 23948682.76 50322399260 17 12912100476 0 12912100476 g Other regions Total
Other sectors 1.2012E+12 1.72245E+11 1.37345E+12 Other sectors 1.25988E+12 1.50981E+11 1.41086E+12 27 1.9794E+11 2697718357 2.00638E+11 Total 1.2515E+12 1.72269E+11 1.42377E+12 Total 1.27279E+12 1.50981E+11 1.42377E+12 Other sectors 1.09723E+12 1.25906E+11 1.22313E+12
E07 6064797475 E17 1369232774 Total 1.29517E+12 1.28604E+11 1.42377E+12
E07* 48543783300 E17* 12795001445 E27 15425060388
S07 0.124934586 S17 0.1070131 E27* 1.72364E+11
S27 0.089491307
Sector 08 g= {lag} Sector 18 g= {lag}
g Other regions Total g Other regions Total Sector 28 g= {lag}
08 33589595197 673794616.9 34263389814 18 393239.6645 0 393239.6645 g Other regions Total
Other sectors 1.21571E+12 1.73803E+11 1.38951E+12 Other sectors 1.24929E+12 1.74477E+11 1.42377E+12 28 1369907875 74614.2533 1369982489 Total 1.2493E+12 1.74477E+11 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12 Other sectors 1.24793E+12 1.74477E+11 1.4224E+12
E08 3525032364 E18 48189.78281 Total 1.2493E+12 1.74477E+11 1.42377E+12
E08* 33438833805 E18* 393239.5559 E28 167810684.6
S08 0.105417324 S18 0.122545614 E28* 1368664264
S28 0.122609093
Sector 09 g= {val} Sector 19 g= {lag}
g Other regions Total g Other regions Total Sector 30 g= {lag}
09 8763618972 59875635167 68639254139 19 14873739000 394234115.9 15267973116 g Other regions Total
Other sectors 1.06436E+11 1.2487E+12 1.35513E+12 Other sectors 1.23442E+12 1.74083E+11 1.4085E+12 30 24972182345 0 24972182345
Total 1.152E+11 1.30857E+12 1.42377E+12 Total 1.2493E+12 1.74477E+11 1.42377E+12 Other sectors 1.22432E+12 1.74477E+11 1.3988E+12
E09 3209900087 E19 1476788506 Total 1.2493E+12 1.74477E+11 1.42377E+12
E09* 65330194320 E19* 15104245371 E30 3060230572
S09 0.049133484 S19 0.097773074 E30* 24534183967
S30 0.124733334
Sector 10 g= {nzi, val} Sector 20 g= {lac, lag}
g Other regions Total g Other regions Total Sector 31 g= {lag}
10 35920223538 38940654909 74860878447 20 42853765156 153890728.3 43007655884 g Other regions Total
Other sectors 79743718905 1.26917E+12 1.34891E+12 Other sectors 1.20816E+12 1.72609E+11 1.38076E+12 31 342141318.1 0 342141318.1 Total 1.15664E+11 1.30811E+12 1.42377E+12 Total 1.25101E+12 1.72763E+11 1.42377E+12 Other sectors 1.24895E+12 1.74477E+11 1.42343E+12
E10 29838699648 E20 5064741799 Total 1.2493E+12 1.74477E+11 1.42377E+12
E10* 70924749252 E20* 41708530553 E31 41927906.3
S10 0.420709272 S20 0.121431797 E31* 342059099.4
S31 0.122575036
Sector 32 g= {lag, mon, den, wor, hsa}
g Other regions Total
32 4885579619 288253117.2 5173832736 Other sectors 1.25666E+12 1.61939E+11 1.4186E+12 Total 1.26154E+12 1.62227E+11 1.42377E+12
E32 301263474.5
E32* 5155031592
S32 0.058440665
REGIONS SPECIALIZATION COEFFICIENTS (1980-1996)
Agnéby g= {01, 02, 03, 11, 13} N'Zi Comoé g= {03, 06, 10, 13, 27}
g Other sectors total g Other sectors total
Agnéby 12883343194 520393913.1 13403737108 Nzi 463551617.7 562818.9359 464114436.6
Other regions 1.52412E+11 1.25796E+12 1.41037E+12 Other regions 3.46419E+11 1.07689E+12 1.42331E+12 total 1.65295E+11 1.25848E+12 1.42377E+12 total 3.46882E+11 1.07689E+12 1.42377E+12
Eagn 11327212601 Enzi 350476549.2
Eagn* 13277551068 Enzi* 463963146.8
Sagn 0.853110076 Snzi 0.755397388
Bas sassandra g= {03, 06, 13, 16, 17, 27} Savanes g= {01, 02, 06, 27}
g Other sectors total g Other sectors total
Bas 20266444582 3230031355 23496475937 Sav 2574361284 187780540.7 2762141825
Other regions 2.77387E+11 1.12289E+12 1.40028E+12 Other regions 2.42266E+11 1.17874E+12 1.42101E+12
total 2.97654E+11 1.12612E+12 1.42377E+12 total 2.4484E+11 1.17893E+12 1.42377E+12
Ebas 15354270794 Esav 2099367055
Ebas* 23108714186 Esav* 2756783224
Sbas 0.664436397 Ssav 0.761527797
Lacs g= {01, 03, 06, 20, 23, 26, 27} Sud bandama g= {03, 06, 13, 27}
g Other sectors total g Other sectors total
Lac 1435289664 278415368.9 1713705033 Sba 1101941906 58129749.07 1160071655
Other regions 4.57446E+11 9.64613E+11 1.42206E+12 Other regions 2.7092E+11 1.15169E+12 1.42261E+12 total 4.58881E+11 9.64891E+11 1.42377E+12 total 2.72022E+11 1.15175E+12 1.42377E+12
Elac 882963635.5 Esba 880302181.9
Elac* 1711642354 Esba* 1159126443
Slac 0.515857553 Ssba 0.75945311
Lagunes g= {04, 05, 07, 08, 12, 14, 15, 18, 19, 20, 21, 22, Vallée banda. g= {09, 10, 11}
23, 24, 25, 26, 27, 28, 30, 31, 32} g Other sectors total
g Other sectors total Val 1.08308E+11 6892135324 1.152E+11
Lag 1.02215E+12 2.27145E+11 1.2493E+12 Other regions 1.36394E+11 1.17218E+12 1.30857E+12 Other regions 29168091630 1.45309E+11 1.74477E+11 total 2.44701E+11 1.17907E+12 1.42377E+12
total 1.05132E+12 3.72454E+11 1.42377E+12 Eval 88508496789
Elag 99666352612 Eval* 1.05879E+11
Elag* 1.53096E+11 Sval 0.835941522
Slag 0.651007293
Worodougou g= {23, 27, 32}
Montagnes g= {03, 05, 27, 32} g Other sectors total
g Other sectors total Wor 182571581 0 182571581
Mon 3744158629 97563897.99 3841722527 Other regions 2.84784E+11 1.13881E+12 1.42359E+12 Other regions 2.64859E+11 1.15507E+12 1.41993E+12 total 2.84967E+11 1.13881E+12 1.42377E+12
total 2.68603E+11 1.15517E+12 1.42377E+12 Ewor 146030021.8
Emon 3019393990 Ewor* 182548169.7
Emon* 3831356520 Swor 0.799953361
Smon 0.7880744
Zanzan g= {06, 24, 27}
Denguélé g= {06, 27, 32} g Other sectors total
g Other sectors total Zan 337684013.2 2693377.013 340377390.2
Den 124857492 1389947.866 126247439.9 Other regions 4.23655E+11 9.99777E+11 1.42343E+12 Other regions 2.31433E+11 1.19221E+12 1.42365E+12 total 4.23993E+11 9.99779E+11 1.42377E+12
total 2.31558E+11 1.19221E+12 1.42377E+12 Ezan 236321195.5
Eden 104325023.1 Ezan* 340296017.1
Eden* 126236245.4 Szan 0.694457718
Sden 0.826426854
Sud comoé g= {02, 26, 27}
Marahoué g= {06, 24, 27} g Other sectors total
g Other sectors total Sco 861220883.2 60498696.36 921719579.6
Mar 466324839.4 0 466324839.4 Other regions 3.14775E+11 1.10808E+12 1.42285E+12
Other regions 4.23526E+11 9.99779E+11 1.42331E+12 total 3.15636E+11 1.10814E+12 1.42377E+12
total 4.23993E+11 9.99779E+11 1.42377E+12 Esco 656885005.3
Emar 327455452 Esco* 921122878.1
Emar* 466172105.1 Ssco 0.713135045
Smar 0.702434677
Haut sassan. g= {02, 03, 06, 13, 16, 23, 27, 32}
Moyen comoé g= {01, 06, 07, 13, 16, 27} g Other sectors total
g Other sectors total Hsa 7248408195 941510771 8189918966
MCO 2081355837 126671729.1 2208027566 Other regions 3.89146E+11 1.02644E+12 1.41558E+12 Other regions 3.4171E+11 1.07985E+12 1.42156E+12 total 3.96394E+11 1.02738E+12 1.42377E+12
total 3.43791E+11 1.07998E+12 1.42377E+12 Ehsa 4968243770
Emco 1548194842 Ehsa* 8142808357
Emco* 2204603292 Shsa 0.610138855
Smco 0.702255525
Annex 3: Theoretical labor productivity function
We started with a Cobb-Douglas version of a Trans-Log production function à la Henderson (1988), as following :
( ) ( )
SX
KX
=g
* , with : (1)- X*(K) : a combination of production factors with constant return to scale in each sector ;
- K : a vector of inputs ;
- g(S) : technical progress is assumed Hicks neutral. It measures specific characteristics of economic activities such as size and technology endowment in an urban area ; g(S) represents external scale economies.
The assumption of technical progress was admitted regarding the regional development policy undertaken by the Ivorian government, based mainly on building capital intensive agro-industries. According to Bohoun and Kouassy (1997), a relatively high capital-labor ratio could have led to a regional capital accumulation. However, the long-term technology diffusion effects were likely limited by the combination of extensive capital accumulation and disinvestments due to a long period (1980-1993) of economic recession. Beyond observed low productivity gains in all the regions, the main issue is to analyse the determinants of regional disparities. Therefore, we assume that regional disparities depend on region-specific characteristics, such as the spatial organization of economic activities.
Dividing equation 1 by the number of employees, we get equation 2 as following :
( ) ( )
SX
kN g
X
0= * , where (2)- N0measures the number of employees in the sector at the level of the region ; - k represents a vector of ratio of inputs to the number of employees.
Putting equation 2 in the logarithmic form, with log[X*(k)]=f(logk) and using a Taylor limited development of first order around each input set as an unity (ki=1), we get a Cobb-Douglas equation as following :
( X N ) C
log[ ]
g( )
S i[
log( )
ki]
log 0 = 0+ +
∑
α (3)With equation 3, one can define the components of g(S) as a function of agglomeration effects and the vector of kivariables:
( ) e N
g
S = ε0 εN where: (4)- ε0= d(logX)/d(logN0) =φ/N0[13]
;
13 X=e-φ/N0NεNX*(K) ; the logarithmic form is : logX=-φ/N0+εNlogN+log(X*(K)) from we derive : d(logX)/d(log N0)=ε0=[d(logX)/dN0] N0= N0d(-φ/N0)/ dN0= N0φ/N02=φ/N0.
- N = whole population of the region.
ε0 and εN correspond to the elasticity of the production of each sector in the region relative, respectively to N0and N, other variables remaining constant.
The logarithmic form of g(S) is as in equation 5 :
( )
(
g S)
N N logNlog =−φ 0 +ε (5)
ε0 is a decreasing function of N0. The localization economies are defined by 1/N0, due to potential colinearity between N0 and N, which takes partly into account urbanization economies. According to Henderson (1988), this definition reduces the colinearity problem. In addition, it shows that sectoral productivity gains at regional level depend positively on the improvement of localization economies. The second component of the right side of equation 4 measures the impacts (positive or negative) of the urbanization economies on productivity gains. However, both of these variables are not relevant enough to identify all the causation links between agglomeration economies and urban/regional productivity gains. Indeed, the colinearity problem mentioned above is not a big issue for Henderson’s model, due to the fact that it leads to estimation errors, but with very limited bias on the convergence of estimators. The limits of these variables are rather in their economic relevance. N identify demand effects as well as urbanization economies. 1/N0 could also correspond to a standard production input. As such, it overestimates the impacts of localization economies, ignoring the human capital component of labor and the overall economic size of the region. Solving these limits suggests clarifying the components of the vector of ratios of inputs kias in Catin’s (1991, 1997) empirical models.
The capital-labor is an important source of the productivity differences between regions (Catin, 1997). In general, reducing regional development disparities and improving the level of labor productivity, require an increase in capital-labor and capital-output ratios (capital coefficient). However, measuring the stock of private investment is extremely difficult in developing countries, due to weakness of the data systems. We used a proxy, the gross cumulative private investment, which does not distinguish amortization of equipment and its residual value. We assume that with long series (1980-1996), most of the old/initial equipment is retrenched from the stock or renewed. We also assume that the technology used in each region is evolving, depending on the level of education, the working experience of employees and regional specificities. Therefore, the quality of labor becomes a variable of localization economies, but there was no relevant variable available for our model. We consider that this lack of variable has a minor impact on the quality of the econometric estimation. According to Hugon (2000), the causal link between education and labor productivity is ambiguous in Côte d’Ivoire, due to a misalignment between the academic content of education and market demand. There are many reasons explaining this observation: the weak rate of conversion of graduates into rural workers, the weak link between education and productivity in the public sector, the high unemployment rate of new graduates, the weak link between the quality of education and knowledge acquired, and the poor use of graduates in the whole economic system.
Annex 4: Econometric tests of the model
We used econometrics of panel data to estimate the model in order to take into account differences between regions and sectors14. In this context, the spatial dimension changes, depending on the number of regions where the sector is available. The econometrics of panel data is expected to improve the quality of estimates by including regional specificities, and allowing different methodology regarding the characteristics of the residuals. In addition, including the spatial dimension reduces risks of stochastic trends (Varoudakis and Véganzonès, 1998). We assume that the residuals of the reduced form of our model are randomly distributed, serially independent, and with minimum and constant variances. In addition, independent variables are assumed to be exogenous.
However, these assumptions imply some tests in order to identify the right econometric method to use. The first range of tests correspond to the specification tests to check for existence (or lack) of individual and/or temporal specificities. These tests are known as heteroskedasticity and serial independence tests. We ran a Breusch-Pagan (BPml) test. A high value (low value) of Breusch-Pagan statistic, associated with a low probability (high probability) will suggest that we include (exclude) regions specificities in (from) the model. The table below shows that the Breusch-Pagan test rejects the assumption of lack of regional specificities in widely spread sectors in the country. Two sectors are concerned: S06 (grain and flavour processing industries) and S27 (trade). The reason for this result is that, from one region to another, economic activities can show different characteristics, despite belonging to the same economic sector.
Breusch-Pagan Test applied to the reduced form of the labor productivity equation
Results Sector number as in annex 1 Regions concerned
BPml Probability Primary sector
S01 Agnéby, Lagoons, Savannah 1,58 0,2093
S02 Agnéby, Lower-Sassandra, Lagoons, South-Comoé 1,45 0,2282
S03 Agnéby, Lower-Sassandra, Upper-Sassandra, Lagoons, Mountains
1,04 0,3085 Secondary sector
S06 Lower-Sassandra, Upper-Sassandra, Lakes, Lagoons, Middle Comoé, South Bandama, Valley of Bandama, Zanzan
3,30 0,0693
S11 Agnéby, Lagoons, Valley of Bandama 1,52 0,2173
S13 Agnéby, Lower-Sassandra, Upper-Sassandra, Lagoons 1,87 0,1715
S16 Lower-Sassandra, Upper-Sassandra, Lagoons 1,51 0,2188
Tertiary sector
S24 Lower-Sassandra, Upper-Sassandra, Lagoons, Valley of Bandama, Zanzan
0,08 0,7767 S26 Lower-Sassandra, Upper-Sassandra, Lakes, Lagoons,
Valley of Bandama
1,84 0,1755 S27 Agnéby, Lower-Sassandra, Upper-Sassandra, Lakes,
Lagoons, Marahoué, Mountains, N’Zi Comoé, Savannah, South Bandama, South Comoé, Valley of Bandama, Zanzan
38,38 0,0000
14Panel data are compiled using annual data of economic sectors covering the period 1980-1996 (17 years). For each sector, the spatial dimension (the number of the region) is repeated each year.
In addition to the Breusch-Pagan test, we ran a Hausman specification test to check the exogeneity of independent variables. The goal of this test is to know if regional specificities are random or constant. If this test failed in rejecting endogeneity of independent variables, while regional specificities are admitted by the Breusch-Pagan test, then one cannot use the General Least Squares (GLS) method, due to bias and non-convergence of estimators. These distortions can be corrected by generating new independent variables as the difference between each original variable and its average annual value. This approach is known as the WITHIN method. It helps to distinguish sectors which should use the GLS method (meaning that regional specificities are random) from the others (where regional specificities are constant). A high value (H) of Hausman test statistic (low probability) rejects exogeneity of independent variables,
In addition to the Breusch-Pagan test, we ran a Hausman specification test to check the exogeneity of independent variables. The goal of this test is to know if regional specificities are random or constant. If this test failed in rejecting endogeneity of independent variables, while regional specificities are admitted by the Breusch-Pagan test, then one cannot use the General Least Squares (GLS) method, due to bias and non-convergence of estimators. These distortions can be corrected by generating new independent variables as the difference between each original variable and its average annual value. This approach is known as the WITHIN method. It helps to distinguish sectors which should use the GLS method (meaning that regional specificities are random) from the others (where regional specificities are constant). A high value (H) of Hausman test statistic (low probability) rejects exogeneity of independent variables,